Introduction to Probability and Data

Hi welcome to my journey of statistics with R - Subhrojit Nandan

First lesson with Statistics with R

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co-relation does not imply causation, what determines whether we can infer causation or just corelation is the type of study that we’re basing our conclusions. Observational studies for the most part allows us to only make corelational statements.

While experiments allow us to infer causation. It is said for the most parts, because there are actually more advanced methods broadfly titled causal inference that allow for making causal infernces for observational studies.

library(tidyverse)
library(dplyr)
library(ggplot2)
library(statsr)

data(present)
View(present)

arbuthnot <- arbuthnot %>%
  mutate(more_boys = boys > girls)

range(present$year)

arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)

arbuthnot <- arbuthnot %>%
  mutate(more_boys = boys > girls)

more_boys <- boys > girls

Error: object 'boys' not found

present <- present %>%
  mutate(total = boys + girls)
View(present)

present <- present %>%
  mutate(prop_boys = boys / total)
View(present)

ggplot(data = present, aes(x = year, y = prop_boys)) +
  geom_point() + 
  geom_smooth()

present <- present %>%
  mutate(more_boys = boys > girls)
View(present)

present <- present %>%
  mutate(prop_boy_girl = boys / girls)
View(present)

ggplot(data = present, aes(x = year, y = prop_boy_girl)) +
  geom_point() + 
  geom_smooth()

present <- present %>%
  arrange(desc(total))
View(present)

library(shiny)

Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio

sfo_feb_flights <- nycflights %>%
  filter(dest == "SFO", month == 2)

View(sfo_feb_flights)

rdu_flights %>%
  group_by(origin) %>%
  summarise(mean_dd = mean(dep_delay), sd_dd = sd(dep_delay), n = n())

sfo_feb_flights %>%
  group_by(carrier) %>%
  summarise(median_dd = median(arr_delay), iqr_dd = IQR(arr_delay), n = n())

ggplot(nycflights, aes(x = factor(month), y = dep_delay)) +
  geom_boxplot()

nycflights <- nycflights %>%
  mutate(dep_type = ifelse(dep_delay < 5, "on time", "delayed"))

nycflights %>%
  group_by(origin) %>%
  summarise(ot_dep_rate = sum(dep_type == "on time") / n()) %>%
  arrange(desc(ot_dep_rate))

ggplot(data = nycflights, aes(x = origin, fill = dep_type)) +
  geom_bar()

nycflights %>% 
  select(avg_speed, tailnum)

Error in .f(.x[[i]], ...) : object 'avg_speed' not found